Service development project to pilot a digital technology innovation for video direct observation of therapy in adult patients with asthma
Bibliographic record
Abstract
BACKGROUND: Adherence to pharmacotherapy and use of the correct inhaler technique are important basic principles of asthma management. Video- or remote-direct observation of therapy (v-DOT) could be a feasible approach to facilitate monitoring and supervising therapy, supporting the delivery of standard care. OBJECTIVE: To explore the utility and the feasibility of v-DOT to monitor inhaler technique and adherence to treatment in adults attending the asthma outpatient service in a tertiary hospital in Northern Ireland. METHOD: The project evaluated use of the technology with 10 asthma patients. Patient and clinician feedback was obtained, in addition to measures of patient engagement and disease-specific clinical markers to assess the feasibility and utility of v-DOT technology in this group of patients. RESULTS: The engagement rate with v-DOT for participating patients averaged 78% (actual video uploads vs expected video uploads) over a median 7 week usage period. Although 50% of patients reported a technical issue at some stage during the usage period, all patients and clinicians reported that the technology was easy to use and that they were satisfied with the outcomes. A range of positive impacts were observed, including optimised inhaler technique and an observed improvement in lung function. An increase in asthma control test scores aligned with clinical aims to promote adherence and alleviate symptoms. CONCLUSION: The v-DOT technology was shown to be a feasible method of assessing inhaler technique and monitoring adherence in this small group of adult asthma patients. A range of positive impacts for participating patients and clinicians were observed. Not all patients invited to join the project agreed to participate or engage with using the technology, highlighting that in this setting, digital modes of delivering care provide only one of the approaches in the necessary "tool kit" for clinicians and patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".